675 citations · 1.8k across the 55 of their papers we have counts for
25 papers · 1 filter
Hierarchical VAE with a Diffusion-based VampPrior
Anna Kuzina, Jakub M. Tomczak
Deep hierarchical variational autoencoders (VAEs) are powerful latent variable generative models. In this paper, we introduce Hierarchical VAE with Diffusion-based Variational Mixt…
Variational Stochastic Gradient Descent for Deep Neural Networks
Haotian Chen, Anna Kuzina, Babak Esmaeili +1
Current state-of-the-art optimizers are adaptive gradient-based optimization methods such as Adam. Recently, there has been an increasing interest in formulating gradient-based opt…
Mixed Models with Multiple Instance Learning
Jan P. Engelmann, Alessandro Palma, Jakub M. Tomczak +2
Predicting patient features from single-cell data can help identify cellular states implicated in health and disease. Linear models and average cell type expressions are typically…
De Novo Drug Design with Joint Transformers
Adam Izdebski, Ewelina Weglarz-Tomczak, Ewa Szczurek +1
De novo drug design requires simultaneously generating novel molecules outside of training data and predicting their target properties, making it a hard task for generative models.…
Exploring Continual Learning of Diffusion Models
Michał Zając, Kamil Deja, Anna Kuzina +4
Diffusion models have achieved remarkable success in generating high-quality images thanks to their novel training procedures applied to unprecedented amounts of data. However, tra…
Discouraging posterior collapse in hierarchical Variational Autoencoders using context
Anna Kuzina, Jakub M. Tomczak
Hierarchical Variational Autoencoders (VAEs) are among the most popular likelihood-based generative models. There is a consensus that the top-down hierarchical VAEs allow effective…
Learning Data Representations with Joint Diffusion Models
Kamil Deja, Tomasz Trzcinski, Jakub M. Tomczak
Joint machine learning models that allow synthesizing and classifying data often offer uneven performance between those tasks or are unstable to train. In this work, we depart from…
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
Emile van Krieken, Thiviyan Thanapalasingam, Jakub M. Tomczak +2
We study the problem of combining neural networks with symbolic reasoning. Recently introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as DeepProbLog, perfo…
Towards a General Purpose CNN for Long Range Dependencies in D
David W. Romero, David M. Knigge, Albert Gu +4
The use of Convolutional Neural Networks (CNNs) is widespread in Deep Learning due to a range of desirable model properties which result in an efficient and effective machine learn…
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models
Kamil Deja, Anna Kuzina, Tomasz Trzciński +1
Diffusion-based Deep Generative Models (DDGMs) offer state-of-the-art performance in generative modeling. Their main strength comes from their unique setup in which a model (the ba…
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC
Anna Kuzina, Max Welling, Jakub M. Tomczak
Variational autoencoders (VAEs) are latent variable models that can generate complex objects and provide meaningful latent representations. Moreover, they could be further used in…
CKConv: Continuous Kernel Convolution For Sequential Data
David W. Romero, Anna Kuzina, Erik J. Bekkers +2
Conventional neural architectures for sequential data present important limitations. Recurrent networks suffer from exploding and vanishing gradients, small effective memory horizo…
General Invertible Transformations for Flow-based Generative Modeling
Jakub M. Tomczak
In this paper, we present a new class of invertible transformations with an application to flow-based generative models. We indicate that many well-known invertible transformations…
Invertible DenseNets
Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai
We introduce Invertible Dense Networks (i-DenseNets), a more parameter efficient alternative to Residual Flows. The method relies on an analysis of the Lipschitz continuity of the…
Super-resolution Variational Auto-Encoders
Ioannis Gatopoulos, Maarten Stol, Jakub M. Tomczak
The framework of variational autoencoders (VAEs) provides a principled method for jointly learning latent-variable models and corresponding inference models. However, the main draw…
Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series
David W. Romero, Erik J. Bekkers, Jakub M. Tomczak +1
Leveraging the symmetries inherent to specific data domains for the construction of equivariant neural networks has lead to remarkable improvements in terms of data efficiency and…
The Convolution Exponential and Generalized Sylvester Flows
Emiel Hoogeboom, Victor Garcia Satorras, Jakub M. Tomczak +1
This paper introduces a new method to build linear flows, by taking the exponential of a linear transformation. This linear transformation does not need to be invertible itself, an…
Learning Discrete Distributions by Dequantization
Emiel Hoogeboom, Taco S. Cohen, Jakub M. Tomczak
Media is generally stored digitally and is therefore discrete. Many successful deep distribution models in deep learning learn a density, i.e., the distribution of a continuous ran…
Attention-based Deep Multiple Instance Learning
Maximilian Ilse, Jakub M. Tomczak, Max Welling
Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as lear…
Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification
Jakub M. Tomczak, Maximilian Ilse, Max Welling
The computer-aided analysis of medical scans is a longstanding goal in the medical imaging field. Currently, deep learning has became a dominant methodology for supporting patholog…
VAE with a VampPrior
Jakub M. Tomczak, Max Welling
Many different methods to train deep generative models have been introduced in the past. In this paper, we propose to extend the variational auto-encoder (VAE) framework with a new…
Improving Variational Auto-Encoders using Householder Flow
Jakub M. Tomczak, Max Welling
Variational auto-encoders (VAE) are scalable and powerful generative models. However, the choice of the variational posterior determines tractability and flexibility of the VAE. Co…
Improving neural networks with bunches of neurons modeled by Kumaraswamy units: Preliminary study
Jakub Mikolaj Tomczak
Deep neural networks have recently achieved state-of-the-art results in many machine learning problems, e.g., speech recognition or object recognition. Hitherto, work on rectified…
Subspace Restricted Boltzmann Machine
Jakub M. Tomczak, Adam Gonczarek
The subspace Restricted Boltzmann Machine (subspaceRBM) is a third-order Boltzmann machine where multiplicative interactions are between one visible and two hidden units. There are…